Data Modelling is a branch of software engineering mainly concerned with presenting complicated data models of a software system into simplified diagrams for visual representation. The complexity of data models is cushioned by incorporating formal techniques and using texts and symbols to represent data across segments.
The goal of data modeling in data science is to illustrate the types of data stored, the association among the data types, and how it can be segmented to draw favorable conclusions for the betterment of the businesses.
What is Data Modelling in Data Science?
The data modeling process begins by gathering information about the requirements of the business from shareholders and end users. These insights are then converted to data structures to form a solid database design. Therefore, most renowned firms like Amazon, Google, and Netflix now leverage data for business purposes. If you wish to join such organizations and start your data modeling journey, you can check out this Power BI basics free course.
This course will guide you through the fundamentals of collecting raw data and using it to get meaningful insights. These insights are then sequenced in such a way as to devise a roadmap that provides knowledge and a concrete understanding of the desired course of action to attain favorable outcomes.
Types of Data Models
Data models form the basis for software development and analytics. These models define a standardized method for formatting database content across systems. Data models are built so they can be placed into the design of a new or an existing business model. The term “Data Model” refers to the process in which data is standardized, organized, and documented within a database. It provides tools for better understanding a database’s design at every level of data abstraction.
Therefore the following data models are used to understand the structure of the database:
1. Conceptual Data Model
A conceptual data model overviews the database concepts and their relationship. Creating a conceptual data model aims to set up entities, attributes, and relationships. Under conceptual data modeling, no detail on the actual database structure will be available.
The conceptual data model has three basic tenets:
Entity: A real-world thing
Attribute: Properties of an entity
Relationship: Dependency between two entities
Characteristics of Conceptual data model:
- It offers an organization-wide view of the business concepts.
- The type of data is curated for a business audience.
- Such models are developed independently of hardware specifications. The main aim is to present data as how a user will see it in the real world.
2. Logical Data Model
A logical data model sets up data elements’ structure and relationships. It’s a self-sufficient, independent model that shows how the data will be implemented. It acts as a blueprint for the data. The logical data model adds more information to the elements of conceptual data modeling in data science.
The three main components of a logical data model are:
Entities: Every entity presents a set of things, persons, or concepts relevant to the business.
Relationships: Every relationship represents the link between two of the above entities.
Attributes: Every attribute defines characteristics that help describe an entity.
Characteristics of the logical data model:
- It can be designed independently of the database management system.
- Data types in the data attributes are of exact lengths and precisions.
- A logical data model must be designed in such a way that any changes in the technology should not affect it.
3. Physical Data Model
A Physical data model is used to describe a database-specific implementation of the data model. It offers abstractions and also helps to generate schemas. In addition, the physical data model helps visualize the database structure by cloning database column keys, triggers, and other RDBMS features.
Characteristics of a physical data model:
- A physical data model describes the data needed for a single project or application. However, there could be a case where it can be integrated with other physical data models.
- It forms the basis of the relationship between the tables that address the credibility and nullability of the connections.
- Things like primary and foreign keys, indexes, access profiles, authorizations, etc., are defined.
What are Data Modelling techniques?
Broadly there are five primary techniques used to organize the data:
1. Hierarchical Data Model
Under this model, the data is stored in a tree-like structure where the nodes are stored in a defined order. It is an arrangement where items are represented as “above,” “below,” or “at the same level” as each other. The hierarchy begins at the root and starts growing like a tree. This model is known to explain real-time relationships between two distinct data.
2. Network Model
Data under the network model is arranged in a graph-like structure where child nodes can have multiple parent nodes. The network model enables many to many relationships among the connected nodes. The parent nodes are considered owners, and the child nodes are considered members.
3. Relational Model
Data is arranged in table format under the relational model. Each data table has rows and columns highlighting a particular aspect of an entity. Putting data into tables makes analyzing the relationship between data points considerably convenient.
4. Object-Oriented Database Model
Under the object-oriented database, the model arranges data and their relationships in a single structure labeled as an object. The main aim is to represent real-world problems as objects with different attributes.
5. Object Relational Model
This model is an amalgamation of an object-oriented and relational database model. Combining functionalities of the object-oriented model and the basics of relational data models, this model aims to help organizations to be more data-driven and present data in coherent visual representations.
Data modeling in data science is creating models for the data to be kept in the database for a conceptual representation of Data Objects. The primary purpose of designing a data model is to determine the accurate representation of data objects at all levels. The visual representation of data in graphs and tables lets businesses effectively communicate complex data sets within and across organizations.